Reducing data transfer in parallel processing of SQL window functions

Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. We propose a technique that can be used in the parallel execution of this operator when data is naturally partitioned. The proposed method benefits the cases where the required partitioning is not the natural partitioning employed. Preliminary evaluation shows that we are able to limit data transfer among parallel workers to 14% of the registered transfer when using a naive approach.This work was part-funded by project LeanBigData : Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics (FP7-619606).